AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions
This addresses the challenge of accelerating software delivery for DevOps teams, though it appears incremental by integrating existing AI methods into CI/CD workflows.
The paper tackles the problem of human decision points causing latency and operational toil in CI/CD pipelines by proposing AI-augmented pipelines using LLMs and autonomous agents as co-pilots and decision makers, with results including a reference architecture, policy-as-code guardrails, and a case study showing improved deployment efficiency.
Modern software delivery has accelerated from quarterly releases to multiple deployments per day. While CI/CD tooling has matured, human decision points interpreting flaky tests, choosing rollback strategies, tuning feature flags, and deciding when to promote a canary remain major sources of latency and operational toil. We propose AI-Augmented CI/CD Pipelines, where large language models (LLMs) and autonomous agents act as policy-bounded co-pilots and progressively as decision makers. We contribute: (1) a reference architecture for embedding agentic decision points into CI/CD, (2) a decision taxonomy and policy-as-code guardrail pattern, (3) a trust-tier framework for staged autonomy, (4) an evaluation methodology using DevOps Research and Assessment ( DORA) metrics and AI-specific indicators, and (5) a detailed industrial-style case study migrating a React 19 microservice to an AI-augmented pipeline. We discuss ethics, verification, auditability, and threats to validity, and chart a roadmap for verifiable autonomy in production delivery systems.